Big (pig) data and the internet of the swine things: a new paradigm in the industry

peer-reviewedImplications • Big data collected on farms can be transformed into useful information to improve decision making and maximize productivity. A swine management system consisting of tools (software and devices), with a protocol and standard operative procedures, can generate the necessary information for the decision-making process. • New technologies such as electronic feeders and artificial intelligence systems capturing big data will provide a better understanding of animal requirements and behavior, increasing efficiency and sustainability. • Biosecurity can be improved using tracking devices for farm staff, recording movements real-time to decrease disease risks and consequently, improve health and productive performance

High-performing farms exploit reproductive potential of high and low prolific sows better than low-performing farms

Abstract Background Our objective was to examine the impact of farm effects and sow potential on various aspects of sow performance. We examined the interaction between sow prolificacy groups categorized at parity 1 and farm productivity groups for reproductive performance across parities, and lifetime performance. Data included 419,290 service records of 85,096 sows, on 98 Spanish farms, from first-service as gilts to removal, that were served between 2008 and 2013. Farms were categorized into three productivity groups based on the upper and lower 25th percentiles of the farm means of annualized lifetime piglets weaned per sow over the 6 years: high-performing (HP), intermediate-performing (IP), and low-performing (LP) farms. Also, parity 1 sows were categorized into three groups based on the upper and lower 10th percentiles of piglets born alive (PBA) as follows: 15 piglets or more (H-prolific), 8 to 14 piglets, and 7 piglets or fewer (L-prolific). The farm groups represent farm effects, whereas the sow groups represent sow potential. Linear mixed effects models were performed with factorial arrangements and repeated measures. Results Mean parity at removal (4.8 ± 0.01) was not associated with three farm productivity groups (P = 0.43). However, HP farms had 7.7% higher farrowing rates than LP farms (P <  0.05). As a result, H-prolific and L-prolific sows on HP farms had 29.7 and 30.7 fewer non-productive days during lifetime than the respective sows on LP farms (P <  0.05). Furthermore, the H-prolific and L-prolific sows on HP farms had 4.9 and 6.2 more annualized piglets weaned than respective H-prolific and L-prolific sows on LP farms (P <  0.05), which was achieved by giving birth to 0.8–1.0 and 1.4–1.7 more PBA per litter, respectively, than on HP farms during parities 2–6 (P <  0.05). During the first parity, HP farms had 18.8% H-prolific sows compared to 6.2% on LP farms. Conclusion Farm effects substantially affected lifetime performance of sows. Higher lifetime productivity of sows on HP farms was achieved by higher farrowing rate, fewer non-productive days, more PBA and more piglets weaned per sow, regardless of prolific category of the sows

Culling in served females and farrowed sows at consecutive parities in Spanish pig herds

Abstract Background The objectives of our study were 1) to characterize culling and retention patterns in parities 0 to 6 in served females and farrowed sows in two herd groups, and 2) to quantify the factors associated with by-parity culling risks for both groups in commercial herds. Lifetime data from first-service to removal included 465,947 service records of 94,691 females served between 2008 and 2013 in 98 Spanish herds. Herds were categorized into two groups based on the upper 25th percentile of the herd means of annualized lifetime pigs weaned per sow: high-performing (> 24.7 pigs) and ordinary herds (≤ 24.7 pigs). Two-level log-binomial regression models were used to examine risk factors and relative risk ratios associated with by-parity culling risks. Results Mean by-parity culling risks (± SE) for served females and farrowed sows were 5.9 ± 0.03 and 12.4 ± 0.05%, respectively. Increased culling risks were associated with sows that farrowed 8 or fewer pigs born alive (PBA). Also, farrowed sows in high-performing herds in parities 2 to 6 had 1.5–5.6% higher culling risk than equivalent parity sows in ordinary herds (P < 0.05). Furthermore, sows in parities 1 to 6 that farrowed 3 or more stillborn piglets had 2.2–4.8% higher culling risk than for sows that did not farrow any stillborn piglets (P < 0.05). For served sows, culling risk in parity 1 to 6 sows with a weaning-to-first-service interval (WSI) of 7 days or more were 2.2–3.9% higher than equivalent parity sows with WSI 0–6 days (P < 0.05). With regard to relative risk ratios, served sows with WSI 7 days or more were 1.56–1.81 times more likely to be culled than those with WSI 0–6 days. Conclusion Producers should reduce non-productive days by culling sows after weaning, instead of after service or during pregnancy. Also, producers should pay special attention to sows farrowing stillborn piglets or having prolonged WSI, and reconsider culling policy for mid-parity sows when they farrow 8 or fewer PBA

Characteristics of obese or overweight dogs visiting private Japanese veterinary clinics

Objective: To characterize obese or overweight dogs that visited private Japanese veterinary clinics located in humid subtropical climate zones. Methods: Dogs were categorized into four body condition score groups and five body size groups based on their breed. Multilevel logistic regression models were applied to the data. A Chi-squared test was used to examine whether the percentage of obese or overweight dogs differed between breeds. Results: There were 15.1% obese dogs and 39.8% overweight dogs. Obese dogs were characterized by increased age and female sex, whereas overweight dogs were characterized by increased age and neuter status (P < 0.05). Peak probabilities of dogs being either obese or overweight were between 7 and 9 years of age, with the probabilities then declining as the dogs got older. For example, in toy sized dogs, the probability of dogs being overweight increased from 33.4% to a peak of 55.1% as dog age rose from 1 to 8 years old. Also, in medium, small and toy sized dogs, neutered dogs were more likely to be overweight than intact dogs, whereas neutered small sized dogs were more likely to be obese than intact small sized dogs (P < 0.05). Additionally, the percentages of obese or overweight dogs differed between the 10 selected breeds with the highest percentage of obese or overweight dogs. Conclusions: By taking age, body size, sex and neuter status into account, veterinarians can advise owners about maintaining their dogs in ideal body condition

Additional file 1: of Culling in served females and farrowed sows at consecutive parities in Spanish pig herds

Appendix A) Estimates of factors in the log-binomial regression model for culling risks of served gilts. Appendix B) Estimates of factors in the log-binomial regression models for culling risks of served sows. Appendix C) Estimates of factors in the log-binomial regression models for culling risks of farrowed sows. (DOCX 94 kb